%0 Journal Article
%T Generalized Rough Set Method for Ensemble Feature Selection and Multiple Classiffier Fusion<br>集成特征选择的广义粗集方法与多分类器融合
%A SUN Liang
%A HAN Chong-Zhao
%A SHEN Jian-Jing
%A DAI Ning
%A <br>孙亮
%A 韩崇昭
%A 沈建京
%A 戴宁
%J 自动化学报
%D 2008
%I 
%X For improving the performance of multiple classifier system,a novel method of ensemble feature selection is proposed based on generalized rough set.In the paper,the relative dominance decision reduct(RDDR)with respect to multiple decision tables is presented to obtain the best feature subsets and interclass separability from different feature spaces.Then,the ensemble attribute reduction(EAR)method is proposed for ensemble feature selection.Using the KD- DWV algorithm based on knowledge discovery,the effectiveness of EAR was examined with the vegetation classification on a hyperspectral image.The result of the comparison experiment shows that EAR can be used to improve the generalization of multiple classifier system by combining appropriate multiple classifier fusion algorithm.
%K Ensemble feature selection
%K multiple classifier fusion
%K generalized rough set
%K hyperspectral<br>集成特征选择
%K 多分类器融合
%K 广义粗集
%K 高光谱
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=D40441977E685F827A36625E9BEC35A3&yid=67289AFF6305E306&vid=339D79302DF62549&iid=38B194292C032A66&sid=88D36036CFF69B3C&eid=8ED630AD8C61FAE8&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=13